The present disclosure relates generally to the field of computer science. More particularly, the present disclosure relates to computer modeling of interactions among multiple entities.
Computer-generated models are frequently used to replicate various real-life scenarios. Such models, for example, may be used to model traffic congestion in a particular area during a particular time of day. Using these models, researchers can estimate the effect that a change in certain variables related to the models may have on the outcome of the scenarios being replicated.
Computer models may be limited in their usefulness by various factors, including the availability of information with which to construct the network underlying the model. Social contact networks are a type of network representing interactions between entities within a population. Large-scale social contact networks may be particularly complicated to model because of the difficulty in collecting reliable data regarding entities and social contacts within the population. Some social contact network models have addressed this difficulty by utilizing only small data sets in constructing the social contact network. In some types of network models (e.g., the Internet, the power grid, etc.), where the real network structure is not easily available due to commercial and security concerns, methods have been developed to infer the network structure by indirect measurements. However, such methods may not apply to large-scale social contact networks (e.g., large heterogeneous urban populations) because of the variety of information sources needed to build them.